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2,609 papers
This paper investigates the potential of neuromorphic reinforcement learning (RL) by integrating Spiking Temporal Memory (STM) networks. Traditional RL algorithms often struggle with complex, temporally extended tasks due to their reliance on explicit state representations and the challenges of handling noisy and async...
This paper presents a novel approach to quantum hardware design and control, leveraging reinforcement learning to achieve self-organization and optimal performance. The core concept is that quantum processors, rather than relying on pre-determined calibration, can dynamically adapt their entanglement generation and mai...
This paper proposes a novel approach to neural network optimization centered around dynamically adapting the topology of the network through the adjustment of connections between neurons. The core idea is to create a neural network structure that is specifically tailored to a given task by leveraging reinforcement lear...
Geometric Information Learning is a rapidly growing field with the potential to significantly enhance image recognition and object detection tasks. This paper explores the application of reinforcement learning to learn geometric information learning algorithms, aiming to improve the accuracy of these tasks. We propose...
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This paper presents a novel approach to robot swarm coordination control utilizing Multi-Agent Reinforcement Learning (MARL). The core claim is to design a robust and adaptive robot swarm control system by leveraging the learning capabilities inherent in MARL. The proposed system employs a federated reinforcement learn...
This paper investigates the application of adaptive reinforcement learning (RL) for efficient hardware resource management. Traditional hardware resource management often relies on static configurations and manual tuning, which can be inefficient and unresponsive to dynamic workloads. This research proposes a novel app...
This paper investigates the application of reinforcement learning (RL) to the problem of adaptive model selection within deep learning. Traditional model selection methods often rely on manual tuning or grid searches, which can be computationally expensive and inefficient. We propose a framework where a reinforcement a...
This paper addresses the critical challenge of concept drift in continuous learning systems. Traditional machine learning approaches often assume a static environment, leading to performance degradation when the underlying data distribution changes over time. This work proposes a novel framework for continuous learning...
This paper introduces a novel adaptive differential privacy (DP) mechanism designed to mitigate the limitations of traditional DP approaches, particularly concerning excessive noise addition in scenarios with small datasets. The core idea is to dynamically adjust the noise scale based on the sensitivity of the query an...
Deep Reinforcement Learning (DRL) has achieved remarkable success in various domains, including game playing and robotics. However, the inherent uncertainty and complexity of DRL policies pose significant challenges to safety and reliability. This work introduces a novel approach to formally verify the safety and robus...
This paper presents a novel decentralized multi-agent reinforcement learning (MARL) algorithm designed to address the challenges posed by environmental uncertainty and information asymmetry in multi-agent systems. The core of the algorithm lies in leveraging communication between agents, inspired by approaches like MAD...
This paper proposes a novel approach, Neural Network Adaptive Topology Optimization (NNAUTO), for designing and optimizing neural network architectures. The core idea is to dynamically adjust the topology of a neural network during training using a reinforcement learning (RL) agent, guided by a physics-informed energy-...
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
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